Title: Studying dark matter and cosmology through gravitational lensing
Speaker: Sut-Ieng Tam (NYCU)
Date: November 15 at 14:30
Location: R521, General Building II
Abstract:
In the standard model of cosmology, the matter density of the Universe is dominated by dark matter, an invisible substance that plays a key role in the formation and evolution of cosmic structures. Although dark matter cannot be directly observed, its presence can be inferred through the phenomenon of gravitational lensing. In the first part of this talk, I will discuss how gravitational lensing can be used to detect dark matter and constrain its properties. I will present a novel method to probe the collisionless nature of dark matter through the radial acceleration relation (RAR) in galaxy clusters. By leveraging cosmological hydrodynamical simulations, I will explore RARs in both Cold Dark Matter (CDM) and Self-Interacting Dark Matter (SIDM) models. Comparing the theoretical RAR predictions from these simulations with observational data allows us to place constraints on the self-interaction cross-section of dark matter, offering new insights into its fundamental characteristics. In the second part of my talk, I will introduce a likelihood-free cosmological inference method that I recently developed. This approach integrates machine learning techniques with cluster weak lensing analysis, allowing us to bypass traditional likelihood-based methods. This novel framework has the potential to provide new insights into the S8 tension, offering an alternative perspective on cosmological parameter estimation.
